Background: Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation of animals. Aquatic movement ecology can therefore be limited in taxonomic range and ecological coverage. Methods: Here we present a novel deep-learning based, multi-individual tracking approach, which incorporates Structure-from-Motion in order to determine the 3D location, body position and the visual environment of every recorded individual. The application is based on low-cost cameras and does not require the animals to be confined, manipulated, or handled in any way. Results: Using this approach, single individuals, small heterospecific groups and schools of fish were tracked in freshwater and marine environments of varying complexity. Positional tracking errors as low as 1.09 ± 0.47 cm (RSME) in underwater areas up to 500 m 2 were recorded. Conclusions: This cost-effective and open-source framework allows the analysis of animal behaviour in aquatic systems at an unprecedented resolution. Implementing this versatile approach, quantitative behavioural analysis can be employed in a wide range of natural contexts, vastly expanding our potential for examining non-model systems and species.

Dominant individuals are often most influential in their social groups, affecting movement, opinion, and performance across species and contexts. Yet, behavioral traits like aggression, intimidation, and coercion, which are associated with and in many cases define dominance, can be socially aversive. The traits that make dominant individuals influential in one context may therefore reduce their influence in other contexts. Here, we examine this association between dominance and influence using the cichlid fish Astatotilapia burtoni, comparing the influence of dominant and subordinate males during normal social interactions and in a more complex group consensus association task. We find that phenotypically dominant males are aggressive, socially central, and that these males have a strong influence over normal group movement, whereas subordinate males are passive, socially peripheral, and have little influence over normal movement. However, subordinate males have the greatest influence in generating group consensus during the association task. Dominant males are spatially distant and have lower signal-to-noise ratios of informative behavior in the association task, potentially interfering with their ability to generate group consensus. In contrast, subordinate males are physically close to other group members, have a high signal-to-noise ratio of informative behavior, and equivalent visual connectedness to their group as dominant males. The behavioral traits that define effective social influence are thus highly context specific and can be dissociated with social dominance. Thus, processes of hierarchical ascension in which the most aggressive, competitive, or coercive individuals rise to positions of dominance may be counterproductive in contexts where group performance is prioritized.

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Although methods for tracking animals underwater exist, they frequently involve costly infrastructure investment, or capture and manipulation of animals to affix or implant tags. These practical concerns limit the taxonomic coverage of aquatic movement ecology studies and implementation in areas where high infrastructure investment is impossible. Here we present a method based on deep-learning and structure-from-motion, with which we can accurately determine the 3D location of animals, the structure of the environment in which they are moving. Further behavioural decomposition of the body position and contour of animals subsequently allow quantifying the behavioural states of each interacting animal. This approach can be used with minimal infrastructure and without confining animals to to a fixed area, or capturing and interfering with them in any way. With this approach, we are able to track single individuals (Conger Eel, Conger oceanus), small heterospecific groups (Mullus surmuletus, Diplodus sp.), and schools of animals (Tanganyikan cichlids Lamprologus callipterus) in freshwater and marine systems, and in habitats ranging in environmental complexity. Positional information was highly accurate, with errors as low as 1.67% of body length. Tracking data was embedded in 3D environmental models that could be used to examine collective decision making, obstacle avoidance, and visual connectivity of groups. By analyzing body contour and position, we were also able to use unsupervised classification to quantify the kinematic behavioural states of each animal. The proposed framework allows us to understand animal behaviour in aquatic systems at an unprecedented resolution and a fraction of the cost of established methodologies, with minimal domain expertise at the data acquisition or analysis phase required. Implementing this method, research can be conducted in a wide range of field contexts to collect laboratory standard data, vastly expanding both the taxonomic and environmental coverage of quantitative animal movement analysis with a low-cost, open-source solution. 1/151 Understanding the movement and behaviour of animals in their natural habitats is the 2 ultimate goal of behavioural and movement ecology. By situating our studies in the 3 natural world, we have the potential to uncover the natural processes of selection acting 4 on the behaviour in natural populations, in a manner that cannot be achieved through 5 lab studies alone. The ongoing advance of animal tracking and biologging has the 6 potential to revolutionize not only the scale of data collected from wild systems, but 7 also the types of questions that can subsequently be answered. Incorporating 8 geographical data has already given insights, for example, into the homing behaviour of 9 reef fish, migratory patterns of birds, or the breeding site specificity of sea 10 turtles [7,17,46]. Great advances in systems biology have further been made through 11 the study of movement ecology, understanding migratory patterns of birds traversing 12 their phys...

The ability of an individual to predict the outcome of actions of others and to change own behavior adaptively is called anticipation. There are many examples from mammalian species - including humans - that show anticipatory abilities in a social context, however, it is not clear to what extent fishes can anticipate the actions of their interaction partners and what are the underlying mechanisms for that anticipation. To answer these questions, we let live guppies (Poecilia reticulata) interact repeatedly with an open-loop (non-interactive) biomimetic robot that has been previously shown to be an accepted conspecific. The robot performed always the same zigzag trajectory in the experimental tank that ended in one of the corners, which gave the live fish the possibility to learn both the location of the final destination as well as the specific turning movementof the robot over three consecutive trials. The live fish’s reactions were categorized into a global anticipation which we defined as relative time to reach the robot’s final corner and a local anticipation which was the relative time and location of the live fish’s turns relative to robofish turns. As a proxy for global anticipation, we found that live fish in the last trial reached the robot’s destination corner significantly earlier than the robot. Overall, more than 50% of all fish arrived at the destination before the robot. This is more than a random walk model would predict and significantly more as compared to all other equidistant, yet unvisited corners. As a proxy for local anticipation, we found fish to change their turning behavior in response to the robot over the course of the trials. Initially the fish would turn after the robot, which was reversed in the end, as they began to turn slightly before the robot in the final trial. Our results indicate that live fish are able to anticipate predictably behaving social partners both in regard to final movement locations as well as movement dynamics. Given that fish have been found to exhibit consistent behavioral differences, anticipation in fish could have evolved as a mechanism to adapt to different social interaction partners.

Mapping the eco-evolutionary factors shaping the development of animals’ behavioural phenotypes remains a great challenge. Recent advances in ‘big behavioural data’ research—the high-resolution tracking of individuals and the harnessing of that data with powerful analytical tools—have vastly improved our ability to measure and model developing behavioural phenotypes. Applied to the study of behavioural ontogeny, the unfolding of whole behavioural repertoires can be mapped in unprecedented detail with relative ease. This overcomes long-standing experimental bottlenecks and heralds a surge of studies that more finely define and explore behavioural–experiential trajectories across development. In this review, we first provide a brief guide to state-of-the-art approaches that allow the collection and analysis of high-resolution behavioural data across development. We then outline how such approaches can be used to address key issues regarding the ecological and evolutionary factors shaping behavioural development: developmental feedbacks between behaviour and underlying states, early life effects and behavioural transitions, and information integration across development.

Acquiring high resolution quantitative behavioural data underwater often involves installation of costly infrastructure, or capture and manipulation animals. Aquatic movement ecology can therefore be limited in scope of taxonomic and ecological coverage. Here we present a novel deep-learning based, multi-individual tracking approach, which incorporates Structure-from-Motion in order to determine the 3D location, body position and the visual environment of every recorded individual. The application is based on low-cost cameras and does not require the animals to be confined or handled in any way. Using this approach, single individuals, small heterospecific groups and schools of fish were tracked in freshwater and marine environments of varying complexity. Further, we established accuracy measures, resulting in positional tracking errors as low as 1.09 ± 0.47 cm (RSME) in underwater areas up to 500 m 2 . This cost-effective and open-source framework allows the analysis of animal behaviour in aquatic systems at an unprecedented resolution. Implementing this versatile approach, quantitative behavioural analysis can employed in a wide range of natural contexts, vastly expanding our potential for examining non-model systems and species.Background 1 Understanding the movement and behaviour of animals in their natural habitats is the 2 ultimate goal of behavioural and movement ecology. By situating our studies in the 3 natural world, we have the potential to uncover the processes of selection acting on the 4 behaviour in natural populations in a manner that cannot be achieved through lab 5 studies alone. The ongoing advance of animal tracking and biologging brings the 6 opportunity to revolutionize not only the scale of data collected from wild systems, but 7 also the types of questions that can subsequently be answered. Incorporating 8 geographical data has already given insights, for example, into the homing behaviour of 9 reef fish, migratory patterns of birds, or the breeding site specificity of sea 10 turtles [10,22,51]. Great advances in systems biology have further been made through 11 the study of movement ecology, understanding the decision-making processes at play 12 1/14 within primate groups manoeuvring through difficult terrain or the collective sensing of 13 birds traversing their physical environment [41,52]. Unravelling these aspects of animal 14 movement can also vastly improve management strategies [13,14], for example in the 15 creation of protected areas that incorporate bird migratory routes [48] or by reducing 16 by-catch with dynamic habitat usage models of marine turtles [37]. 17Yet the application of techniques that meet the challenges of working in naturally 18 complex environments is not straightforward, with practical, financial, and analytical 19 issues often limiting the resolution or coverage of data gathered. This is especially true 20 in aquatic ecosystems, where approaches such as Global Positioning System (GPS) tags 21 allow only sparse positioning of animals that surface more or less ...

In oviparous species, the timing of hatching is a crucial decision, but for developing embryos, assessing cues that indicate the optimal time to hatch is challenging. In species with pre-hatching parental care, parents can assess environmental conditions and induce their offspring to hatch. We provide the first documentation of parental hatching regulation in a coral reef fish, demonstrating that male neon gobies ( Elacatinus colini ) directly regulate hatching by removing embryos from the clutch and spitting hatchlings into the water column. All male gobies synchronized hatching within 2 h of sunrise, regardless of when eggs were laid. Paternally incubated embryos hatched later in development, more synchronously, and had higher hatching success than artificially incubated embryos that were shaken to provide a vibrational stimulus or not stimulated. Artificially incubated embryos displayed substantial plasticity in hatching times (range: 80–224 h post-fertilization), suggesting that males could respond to environmental heterogeneity by modifying the hatching time of their offspring. Finally, paternally incubated embryos hatched with smaller yolk sacs and larger propulsive areas than artificially incubated embryos, suggesting that paternal effects on hatchling phenotypes may influence larval dispersal and fitness. These findings highlight the complexity of fish parental care behaviour and may have important, and currently unstudied, consequences for fish population dynamics.